A simulation model to investigate interactions between first season grazing calves and Ostertagia ostertagi

Behavioural response to gastrointestinal parasites of yearling dairy calves at pasture

AIMS

To investigate the association between gastrointestinal parasites (GIP) and animal behaviour in dairy calves under New Zealand pastoral conditions, using animal-mounted, accelerometer-based sensors.

METHODS

Thirty-six, 5-6-month-old, Friesian-Jersey, heifer calves fitted with animal activity sensors to track behaviour were randomly allocated to one of two treatment groups. Half the animals were challenged with an oral dose of 20,000 larvae of Ostertagia ostertagi and Cooperia oncophera once a week for 3 weeks and half were unchallenged. Five weeks after the last dose, seven infected and nine uninfected animals were treated with an oral anthelmintic (AHC) and data collected for a further week. Accelerometer data were classified into minutes per day eating, ruminating, in moderate-high activity or in low activity. Live weight and faecal egg counts (FEC) were recorded weekly over the study period. All animals co-grazed a newly sown pasture not previously grazed by ruminants and were moved every week to fresh grazing. Treatment status was blinded to those managing the animals which were otherwise treated identically.

RESULTS

Complete behavioural records were available from 30/36 calves, (13 challenged and 17 unchallenged). Before treatment with AHC, FEC increased in infected and un-treated calves over the study, while uninfected animals maintained a near zero FEC. There was no difference in live weight gain between the two groups over the study period. Bayesian, multinomial regression predicted differences in animal behaviour between infected and uninfected animals that were not treated with AHC over the 7 weeks following initial infection. Parasitised calves not treated with AHC were less active and spent up to 6 (95% highest density interval (HDI) = 1-11) minutes/day less in low level activity and up to 15 (95% HDI = 7-20) minutes/day less in moderate to high level activity. They ruminated up to 9 (95% HDI = 2-15) minutes/day more and ate up to 10 (95% HDI = 2-19) minutes/day more than control calves that were not treated with AHC. The effect of AHC on time spent in each behaviour differed between infected and uninfected calves and increased the coefficient of dispersion of the behavioural data.

CONCLUSIONS AND CLINICAL RELEVANCE

Small differences in animal behaviour can be measured in calves with GIP. However, to use this to target treatment, further validation studies are required to confirm the accuracy of behavioural classification and understand the complex drivers of animal behaviour in a dynamic and variable pasture-parasite-host environment.

Novel epidemiological model of gastrointestinal nematode infection to assess grazing cattle resilience by integrating host growth, parasite, grass and environmental dynamics

Gastrointestinal nematode (GIN) infections are ubiquitous and often cause morbidity and reduced performance in livestock. Emerging anthelmintic resistance and increasing change in climate patterns require evaluation of alternatives to traditional treatment and management practices. Mathematical models of parasite transmission between hosts and the environment have contributed towards the design of appropriate control strategies in ruminants, but have yet to account for relationships between climate, infection pressure, immunity, resources, and growth. Here, we develop a new epidemiological model of GIN transmission in a herd of grazing cattle, including host tolerance (body weight and feed intake), parasite burden and acquisition of immunity, together with weather-dependent development of parasite free-living stages, and the influence of grass availability on parasite transmission. Dynamic host, parasite and environmental factors drive a variable rate of transmission. Using literature sources, the model was parametrised for Ostertagia ostertagi, the prevailing pathogenic GIN in grazing cattle populations in temperate climates. Model outputs were validated on published empirical studies from first season grazing cattle in northern Europe. These results show satisfactory qualitative and quantitative performance of the model; they also indicate the model may approximate the dynamics of grazing systems under co-infection by O. ostertagi and Cooperia oncophora, a second GIN species common in cattle. In addition, model behaviour was explored under illustrative anthelmintic treatment strategies, considering impacts on parasitological and performance variables. The model has potential for extension to explore altered infection dynamics as a result of management and climate change, and to optimise treatment strategies accordingly. As the first known mechanistic model to combine parasitic and free-living stages of GIN with host feed-intake and growth, it is well suited to predict complex system responses under non-stationary conditions. We discuss the implications, limitations and extensions of the model, and its potential to assist in the development of sustainable parasite control strategies.

Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.

A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows

Fasciola hepatica and Ostertagia ostertagi are internal parasites of cattle compromising physiology, productivity, and well-being. Parasites are complex in their effect on hosts, sometimes making it difficult to identify clear directions of associations between infection and production parameters. Therefore, unsupervised approaches not assuming a structure reduce the risk of introducing bias to the analysis. They may provide insights which cannot be obtained with conventional, supervised methodology. An unsupervised, exploratory cluster analysis approach using the k–mode algorithm and partitioning around medoids detected two distinct clusters in a cross-sectional data set of milk yield, milk fat content, milk protein content as well as F. hepatica or O. ostertagi bulk tank milk antibody status from 606 dairy farms in three structurally different dairying regions in Germany. Parasite–positive farms grouped together with their respective production parameters to form separate clusters. A random forests algorithm characterised clusters with regard to external variables. Across all study regions, co–infections with F. hepatica or O. ostertagi, respectively, farming type, and pasture access appeared to be the most important factors discriminating clusters (i.e. farms). Furthermore, farm level lameness prevalence, herd size, BCS, stage of lactation, and somatic cell count were relevant criteria distinguishing clusters. This study is among the first to apply a cluster analysis approach in this context and potentially the first to implement a k–medoids algorithm and partitioning around medoids in the veterinary field. The results demonstrated that biologically relevant patterns of parasite status and milk parameters exist between farms positive for F. hepatica or O. ostertagi, respectively, and negative farms. Moreover, the machine learning approach confirmed results of previous work and shed further light on the complex setting of associations a between parasitic diseases, milk yield and milk constituents, and management practices.

GLOWORM-PARA: a flexible framework to simulate the population dynamics of the parasitic phase of gastrointestinal nematodes infecting grazing livestock

Gastrointestinal nematodes are a significant threat to the economic and environmental sustainability of keeping livestock, as adequate control becomes increasingly difficult due to the development of anthelmintic resistance in some systems and climate-driven changes to infection dynamics. To mitigate any negative impacts of climate on gastrointestinal nematode epidemiology and slow anthelmintic resistance development, there is a need to develop effective, targeted control strategies that minimise the unnecessary use of anthelmintic drugs and incorporate alternative strategies such as vaccination and evasive grazing. However, the impacts climate and gastrointestinal nematode epidemiology may have on the optimal control strategy are generally not considered, due to lack of available evidence to drive recommendations. Parasite transmission models can support control strategy evaluation to target field trials, thus reducing the resources and lead-time required to develop evidence-based control recommendations incorporating climate stochasticity. Gastrointestinal nematode population dynamics arising from natural infections have been difficult to replicate and model applications have often focussed on the free-living stages. A flexible framework is presented for the parasitic phase of gastrointestinal nematodes, GLOWORM-PARA, which complements an existing model of the free-living stages, GLOWORM-FL. Longitudinal parasitological data for two species that are of major economic importance in cattle, Ostertagia ostertagi and Cooperia oncophora, were obtained from seven cattle farms in Belgium for model validation. The framework replicated the observed seasonal dynamics of infection in cattle on these farms and overall, there was no evidence of systematic under- or over-prediction of faecal egg counts. However, the model under-predicted the faecal egg counts observed on one farm with very young calves, highlighting potential areas of uncertainty that may need further investigation if the model is to be applied to young livestock. The model could be used to drive further research into alternative parasite control strategies such as vaccine development and novel treatment approaches, and to understand gastrointestinal nematode epidemiology under changing climate and host management.

Control of helminth ruminant infections by 2030

Helminth infections have large negative impacts on production efficiency in ruminant farming systems worldwide, and their effective management is essential if livestock production is to increase to meet future human needs for dietary protein. The control of helminths relies heavily on routine use of chemotherapeutics, but this approach is unsustainable as resistance to anthelmintic drugs is widespread and increasing. At the same time, infection patterns are being altered by changes in climate, land-use and farming practices. Future farms will need to adopt more efficient, robust and sustainable control methods, integrating ongoing scientific advances. Here, we present a vision of helminth control in farmed ruminants by 2030, bringing to bear progress in: (1) diagnostic tools, (2) innovative control approaches based on vaccines and selective breeding, (3) anthelmintics, by sustainable use of existing products and potentially new compounds, and (4) rational integration of future control practices. In this review, we identify the technical advances that we believe will place new tools in the hands of animal health decision makers in 2030, to enhance their options for control and allow them to achieve a more integrated and sustainable approach to helminth control in support of animal welfare and production.

Modelling the impacts of pasture contamination and stocking rate for the development of targeted selective treatment strategies for Ostertagia ostertagi infection in calves

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Stocking rate effect on design of targeted selective treatments (TST) was evaluated.

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Initial pasture contamination effect on the design of TST was evaluated.

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Different phenotypic traits and methods of selection for treatment were addressed.

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Benefit was assessed as weight gain/frequency of resistant alleles in helminths.

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Treatment according to threshold triggers of average daily gain was most beneficial.

A simulation study was carried out to assess whether variation in pasture contamination or stocking rate impact upon the optimal design of targeted selective treatment (TST) strategies. Two methods of TST implementation were considered: 1) treatment of a fixed percentage of a herd according to a given phenotypic trait, or 2) treatment of individuals that exceeded a threshold value for a given phenotypic trait. Four phenotypic traits, on which to base treatment were considered: 1) average daily bodyweight gain, 2) faecal egg count, 3) plasma pepsinogen, or 4) random selection. Each implementation method (fixed percentage or threshold treatment) and determinant criteria (phenotypic trait) was assessed in terms of benefit per R (BPR), the ratio of average benefit in weight gain to change in frequency of resistance alleles R (relative to an untreated population). The impact of pasture contamination on optimal TST strategy design was investigated by setting the initial pasture contamination to 100, 200 or 500 O. ostertagi L3/kg DM herbage; stocking rate was investigated at a low (3calves/ha), conventional (5 calves/ha) or high (7 calves/ha) stocking rates. When treating a fixed percentage of the herd, treatments according to plasma pepsinogen or random selection were identified as the most beneficial (i.e. resulted in the greatest BPR) for all levels of initial pasture contamination and all stocking rates. Conversely when treatments were administered according to threshold values ADG was most beneficial, and was identified as the best TST strategy (i.e. resulted in the greatest overall BPR) for all levels of initial pasture contamination and all stocking rates.

Modelling the consequences of targeted selective treatment strategies on performance and emergence of anthelmintic resistance amongst grazing calves

The development of anthelmintic resistance by helminths can be slowed by maintaining refugia on pasture or in untreated hosts. Targeted selective treatments (TST) may achieve this through the treatment only of individuals that would benefit most from anthelmintic, according to certain criteria. However TST consequences on cattle are uncertain, mainly due to difficulties of comparison between alternative strategies. We developed a mathematical model to compare: 1) the most 'beneficial' indicator for treatment selection and 2) the method of selection of calves exposed to Ostertagia ostertagi, i.e. treating a fixed percentage of the population with the lowest (or highest) indicator values versus treating individuals who exceed (or are below) a given indicator threshold. The indicators evaluated were average daily gain (ADG), faecal egg counts (FEC), plasma pepsinogen, combined FEC and plasma pepsinogen, versus random selection of individuals. Treatment success was assessed in terms of benefit per R (BPR), the ratio of average benefit in weight gain to change in frequency of resistance alleles R (relative to an untreated population). The optimal indicator in terms of BPR for fixed percentages of calves treated was plasma pepsinogen and the worst ADG; in the latter case treatment was applied to some individuals who were not in need of treatment. The reverse was found when calves were treated according to threshold criteria, with ADG being the best target indicator for treatment. This was also the most beneficial strategy overall, with a significantly higher BPR value than any other strategy, but its degree of success depended on the chosen threshold of the indicator. The study shows strong support for TST, with all strategies showing improvements on calves treated selectively, compared with whole-herd treatment at 3, 8, 13 weeks post-turnout. The developed model appeared capable of assessing the consequences of other TST strategies on calf populations.

A stochastic model to investigate the effects of control strategies on calves exposed to Ostertagia ostertagi

Predicting the effectiveness of parasite control strategies requires accounting for the responses of individual hosts and the epidemiology of parasite supra- and infra-populations. The first objective was to develop a stochastic model that predicted the parasitological interactions within a group of first season grazing calves challenged by Ostertagia ostertagi, by considering phenotypic variation amongst the calves and variation in parasite infra-population. Model behaviour was assessed using variations in parasite supra-population and calf stocking rate. The model showed the initial pasture infection level to have little impact on parasitological output traits, such as worm burdens and FEC, or overall performance of calves, whereas increasing stocking rate had a disproportionately large effect on both parasitological and performance traits. Model predictions were compared with published data taken from experiments on common control strategies, such as reducing stocking rates, the ‘dose and move’ strategy and strategic treatment with anthelmintic at specific times. Model predictions showed in most cases reasonable agreement with observations, supporting model robustness. The stochastic model developed is flexible, with the potential to predict the consequences of other nematode control strategies, such as targeted selective treatments on groups of grazing calves.